If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Artificial intelligence is no longer a buzz phrase -- it's doing real work for real companies. Even in the early stages of implementation, AI is providing enterprise organizations with benefits: Efficiency in operations, cybersecurity protections, digital innovation, and stronger customer relationships. Next up for AI in the enterprise is the ability to scale with more apps serving more departments. However, the race to implement AI and machine learning also raises citizen privacy concerns. There have been revelations about the potential for algorithmic bias reflected in data sources.
Applitools announced today that its test automation tools have been integrated with GitHub and GitHub Actions in addition to being available on the Microsoft Visual Studio App Center. Company COO Moshe Milman said these integrations make it possible to add testing tools that incorporate machine learning algorithms, dubbed Visual AI, to a DevOps workflow using the Applitools Eyes testing platform. Those tests are run on a cloud platform dubbed Ultrafast Grid. The goal is to make it possible to easily correlate code changes across different versions of updates to web and mobile applications using any testing framework and programming language, said Milman. Visual AI can now be applied to every build and pull request, he noted.
A few years ago, everyone was trying to figure out how to get started with artificial intelligence and one of its components, machine learning. But today many organizations have put together pilot programs, identified promising use cases, and even turned around some value for their organizations. After you've won those initial successes, it's time to expand that value to other use cases and other parts of the organization. But with each of your initial use cases you learned something. You developed some technology that you may want to use again.
The data generated in DevOps runs very well into exabytes. Not only it becomes difficult for the DevOps team to effectively absorb the data but also makes it challenging for them to apply solutions from this massive amount of data. The data generated by continuous integration and tools deployment is humongous. In fact, simple issues such as finding critical events usually take hundreds of hours. The number of integrations, the success rate, and defects per integration is only useful when they are timely evaluated and correlated.
The advent of Machine Learning (ML) and Artificial Intelligence (AI) has changed the way we perceive DevOps. It is providing the type of DevOps that is considered to be the need-to-have framework. For many software development companies, it is crucial to use AI and ML with DevOps to ensure the uninterrupted delivery of high-quality applications and features. Sogeti and Capgemini predicted that by the end of 2020, there will be extensive use of Artificial Intelligence across all the areas of DevOps. AI is being infused in testing and operations to bring efficiency in the detection of problems, AI can also be a great help in the enhancement of DevOps.
Since the days of gigantic data warehouses and big data analysis, experts have been saying how big IT infrastructures are becoming today, and how--soon enough--they will be too massive and complex to manage manually. New tools are being introduced to simplify IT infrastructure management all the time it seems. Containerization, for example, is now an entirely automated process that is completely abstracted from the hardware and software layers underneath it. Regardless of the automation tools and the many options for simplifying IT infrastructure, it is only a matter of time before we need to address the process itself. DevOps specialists are working at capacity.
DevOps engineering is all about accelerating software development processes to deliver value to customers faster, without compromising code quality. Traditional DevOps has come a long way over the past decade and now allows many organizations to implement a CI/CD pipeline. However, in most cases, teams are still relying on a combination of manual processes and human-driven automation processes. This is not as optimized as it can or should be. Watch the on-demand webinar: Lessons from The Phoenix project you can use today.
Thanks to the rapid adoption of Artificial Intelligence (AI) and Machine Learning (ML) across industries, AI and ML have found a place in the common vocabulary. Almost every sector of the industry (healthcare, e-commerce, IoT, banking & finance, etc.) are leveraging AI and ML to streamline business operations and create innovative products/services. So, when everyone in the industry is using AI and ML, what can you do differently to up your game? The answer is MLOps or Machine Learning Operationalization. In simple terms, MLOps is the Machine learning equivalent of DevOps.